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Incorporating Travel Time Reliability Data in Travel Path Estimation Sam Granato, Ohio DOT Rakesh Sharma, Belomar Regional Council. Which travel path to take?.

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Which travel path to take?


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slide1

Incorporating Travel Time Reliability Data in Travel Path EstimationSam Granato, Ohio DOTRakesh Sharma, Belomar Regional Council

which travel path to take
Which travel path to take?
  • Not much to do with “equilibrium” (latest exp. GPS study - only 1/3 of travelers on shortest time path, none on the shortest distance path - unless the same as shortest time path).
  • Why? Plenty of day-to-day variability in both link-level volumes and travel delays (as well as differences in traveler perceptions).
  • Use of variability of travel times in models born out of a sense that delay should somehow get more “weight” than other travel time for impatient drivers (but I never followed thru on some initial ideas for “off-line” weighting via static (& 1-off) link penalties).
how do people select a travel path
How do people select a travel path?
  • Distance (fixed)
  • Travel time (average)
  • Travel time (variability/reliability)
  • Pavement condition
  • Safety (perceived, both on and off-road)
  • “Fear of merging”
  • The “scenic route?”
slide6

Forecasting framework using “not your father’s QRSII”DTA is integrated within 4-step process as a feedback loop instead of a “stand-alone” process, while a separate (3rd) loop estimates the “reliable” travel paths - if non-additive by link.

implementing reliability in path building step
Implementing Reliability in Path Building step:
  • Total Path Impedance (RR = reliability ratio)
  • Variance of Path Travel Time
  • Thru movements on successive links are correlated.
  • Marginal change in travel time standard deviation from selecting the next link:
  • So that for any path between an origin and destination:
details of the link level reliability calcs
Details of the Link-level Reliability calcs:
  • Path correction using MSA, vine building necessary.
  • Convergence found in a finite number of steps (via calculation of “path error” term).
  • Link travel time includes intersection delay, while standard deviation derived from the coefficient of variation equation
  • “Free” travel time t0 includes intersection delays under low-flow conditions. (L=link length)
  • Calibration coefficients can vary by road type
reliability parameters
Reliability Parameters

Link Impedance =all other components + (reliability ratio)*t*CV

wheeling area t ravel d emand model
Wheeling Area Travel Demand Model

150,000 metro population, 900 modeled intersections (125 signalized), validated to local travel time surveys as well as counts

slide14
Simplified Example (average time/”free-flow” time):(All links are 0.25 miles with running speed=30 mph so “running” time=0.5 minutes)
results
Results
  • Number of Path building iterations has clear impact on model volumes but mixed on validation
  • Procedures reduce impact of “worst” intersections
  • Counter-intuitively (?), equation values from UK study worked better than nearby (WWW region) surveys
  • Virtually no differences among options for local travel time error (table shows “bottom line” for volume)
overall impact of incorporating r eliability in the model u pdate
Overall Impact of Incorporating Reliability in the Model Update?
  • Little impact on overall validation v. counts or travel times
  • Regardless of option taken, superior validation

Few differences overall due to relative lack of congestion

modeled travel t ime with procedures
Modeled Travel Time with Procedures
  • Urban street travel time errors (average of 8%) lower than models from other regions (esp. those still relying on fixed capacities & vdfs).
  • Errors even less on freeways (2%) and rural roads (4%)
impact of using new data sources
Impact of using new data sources:
  • Freeway floating car (Cleveland)
  • Local GPS data (Columbus sample)
  • Generated new sets of CV equation coefficients
  • No impact on model’s
  • Validation (so far)
  • Statewide GPS data on curves/grades/RR crossings found less delay than expected
  • Demand-side in future?
questions
Questions?
  • sam.granato@dot.state.oh.us
  • rsharma@belomar.org